1.Objective¶

  • To predict the future stock price of IDEA.NS for next 30 days.

What is LSTM ?¶

  • Long short-term memory is an artificial recurrent neural network architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can process not only single data points, but also entire sequences of data.

  • LSTM is a type of brain-like system called a neural network. It’s special because it can remember things for a long time. This is really useful when you’re dealing with things that change over time, like weather patterns, stock prices, or language.

  • Here’s how it works:

  • Input Gate: Decides what new information we’re going to store in memory.

  • Forget Gate: Decides what information we’re going to throw away.
  • Output Gate: Decides what information we’re going to use to make decisions.
  • So, LSTM looks at new information (input), decides what to remember and what to forget, and then uses that memory to make decisions.

3.Importing Library¶

In [1]:
import pandas as pd
import numpy as np
import math
import datetime as dt
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
In [2]:
df=pd.read_csv(r"C:\Users\Armaan\Downloads\IDEA.NS.csv")
In [3]:
df.head()
Out[3]:
Date Open High Low Close Adj Close Volume
0 28-10-2010 40.700409 41.273228 38.710609 39.434174 38.824368 12280665.0
1 29-10-2010 40.067291 40.790855 39.524620 40.670261 40.041344 5692358.0
2 01-11-2010 40.851151 42.087238 40.851151 41.393822 40.753716 6709782.0
3 02-11-2010 41.574715 41.574715 40.368774 40.519520 39.892933 3966238.0
4 03-11-2010 41.876198 41.876198 39.886398 40.097439 39.477379 4179627.0
In [4]:
df.tail()
Out[4]:
Date Open High Low Close Adj Close Volume
3207 20-10-2023 12.00 12.10 11.65 11.75 11.75 150616671.0
3208 23-10-2023 11.80 11.90 10.85 10.95 10.95 229385426.0
3209 25-10-2023 11.00 11.20 10.60 10.85 10.85 259001685.0
3210 26-10-2023 10.85 10.95 10.50 10.75 10.75 163742432.0
3211 27-10-2023 10.65 11.05 10.65 10.90 10.90 207190392.0

4. Exploratory data analysis.¶

In [5]:
df.describe()
Out[5]:
Open High Low Close Adj Close Volume
count 3206.000000 3206.000000 3206.000000 3206.000000 3206.000000 3.206000e+03
mean 44.186779 45.022857 43.304874 44.117548 43.703432 9.045426e+07
std 32.502808 33.016211 31.960267 32.466469 32.042595 1.595767e+08
min 2.700000 3.150000 2.400000 2.950000 2.950000 0.000000e+00
25% 10.100000 10.362500 9.812500 10.050000 10.050000 6.884168e+06
50% 45.267899 45.991461 44.604631 45.222675 44.870220 1.801413e+07
75% 65.723625 67.080299 64.412164 65.806534 65.181887 1.039615e+08
max 122.402710 123.005676 119.568756 122.251968 120.885239 1.073742e+09
In [6]:
print(df.columns)

Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')
In [7]:
df.isnull().sum()
Out[7]:
Date         0
Open         6
High         6
Low          6
Close        6
Adj Close    6
Volume       6
dtype: int64

Insight¶

  • In the data their are 6 Nan values.
In [8]:
df=df.dropna()
In [9]:
df.isnull().sum()
Out[9]:
Date         0
Open         0
High         0
Low          0
Close        0
Adj Close    0
Volume       0
dtype: int64
In [10]:
df.shape
Out[10]:
(3206, 7)

4.1 Staring Date and End Date.¶

In [11]:
sd=df.iloc[0][0]
ed=df.iloc[-1][0]

print('Starting Date',sd)
print('Ending Date',ed)

Starting Date 28-10-2010
Ending Date 27-10-2023

4.2 Graphical representation¶

In [12]:
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

C:\Users\Armaan\anaconda3\lib\site-packages\scipy\__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.26.1
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"
In [13]:
fig, axs = plt.subplots(2, 2, figsize=(18, 11))

# Create KDE plots with labels
sns.kdeplot(df.Open, shade=True, color='Green', alpha=0.5, ax=axs[0, 0], label='Open')
sns.kdeplot(df.High, shade=True, color='orange', alpha=0.5, ax=axs[0, 1], label='High')
sns.kdeplot(df.Low, shade=True, color='violet', alpha=0.5, ax=axs[1, 0], label='Low')
sns.kdeplot(df.Close, shade=True, color='blue', alpha=0.5, ax=axs[1, 1], label='Close')

# Add legends
axs[0, 0].legend()
axs[0, 1].legend()
axs[1, 0].legend()
axs[1, 1].legend()

plt.show()
In [14]:
import plotly.express as px
from itertools import cycle


names= cycle(['Stock Open Price','Stock Close Price','Stock High Price','Stock Low Price'])


fig = px.line(df, x='Date', y=["Open",'Close',"High","Low"], labels={'Date': 'Date','value':'Stock value'},
              title='IDEA.NS stock prices chart')

fig.update_layout( font_color='black',legend_title_text='Stock Parameters')
fig.for_each_trace(lambda t:  t.update(name = next(names)))



fig.show()
In [15]:
fig = px.line(df, x='Date', y='Volume', labels={'Date': 'Date', 'Volume': 'Stock Volume'},
              title='Stock Volume Over Time')

fig.update_layout(font_size=13,font_color='black')

fig.show()

4.3 Moving Average¶

In [16]:
ma50=df.Close.rolling(50).mean()
ma100=df.Close.rolling(100).mean()

ma100
Out[16]:
0          NaN
1          NaN
2          NaN
3          NaN
4          NaN
         ...  
3207    8.9595
3208    8.9980
3209    9.0345
3210    9.0705
3211    9.1080
Name: Close, Length: 3206, dtype: float64
In [17]:
plt.figure(figsize=(22,8))
plt.plot(df.Open, label='Open')
plt.plot(ma50, "yellow", label='MA50')
plt.plot(ma100, "r", label='MA100')
plt.title('Stock Close Over Time')
plt.grid(True)
plt.legend(loc='best', prop={'size': 16})
plt.show()




plt.figure(figsize=(22,8))
plt.plot(df.Close, label='Close')
plt.plot(ma50, "yellow", label='MA50')
plt.plot(ma100, "r", label='MA100')
plt.title('Stock Open Over Time')
plt.grid(True)
plt.legend(loc='best', prop={'size': 16})
plt.show()
In [18]:
plt.figure(figsize=(22,8))
plt.plot(df.High, label='High')
plt.plot(ma50, "yellow", label='MA50')
plt.plot(ma100, "r", label='MA100')
plt.title('Stock Close Over Time')
plt.grid(True)
plt.legend(loc='best', prop={'size': 16})
plt.show()



plt.figure(figsize=(22,8))
plt.plot(df.Low, label='low')
plt.plot(ma50, "yellow", label='MA50')
plt.plot(ma100, "r", label='MA100')
plt.title('Stock Open Over Time')
plt.grid(True)
plt.legend(loc='best', prop={'size': 16})
plt.show()

Insight¶

  • Moving Average of 50 and 100 were plotted along with the original line plot but i have taken MA100 for further analysis because of its smoothness.
  • Moving averages in stock price analysis help identify trends, smooth out price volatility, and indicate support and resistance levels.

5. MinMaxScaler¶

In [19]:
df1=df.Open
In [20]:
from sklearn.preprocessing import MinMaxScaler
Scaler=MinMaxScaler(feature_range=(0,1))
df1=Scaler.fit_transform(np.array(df1).reshape(-1,1))
In [21]:
df.shape
Out[21]:
(3206, 7)
In [22]:
df1
Out[22]:
array([[0.31745655],
       [0.31216746],
       [0.31871585],
       ...,
       [0.06933845],
       [0.06808534],
       [0.06641454]])

5.1 Train test model¶

In [23]:
training_size=int(len(df1)*0.70)
test_size=len(df1)-training_size
train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]
In [24]:
print("train_data: ", train_data.shape)
print("test_data: ", test_data.shape)

train_data:  (2244, 1)
test_data:  (962, 1)
In [25]:
def create_dataset(dataset, time_step=1):
    dataX = [dataset[i:(i+time_step), 0] for i in range(len(dataset)-time_step-1)]
    dataY = [dataset[i + time_step, 0] for i in range(len(dataset)-time_step-1)]
    return np.array(dataX), np.array(dataY)
In [26]:
# reshape into X=t,t+1,t+2,t+3 and Y=t+4
time_step = 100
X_train, y_train = create_dataset(train_data, time_step)
X_test, y_test = create_dataset(test_data, time_step)

print("X_train: ", X_train.shape)
print("y_train: ", y_train.shape)
print("X_test: ", X_test.shape)
print("y_test", y_test.shape)

X_train:  (2143, 100)
y_train:  (2143,)
X_test:  (861, 100)
y_test (861,)
In [27]:
# reshape input to be [samples, time steps, features] which is required for LSTM
X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)
X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)

print("X_train: ", X_train.shape)
print("X_test: ", X_test.shape)

X_train:  (2143, 100, 1)
X_test:  (861, 100, 1)
In [28]:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
In [29]:
tf.__version__
Out[29]:
'2.14.0'
In [30]:
tf.keras.backend.clear_session()
model=Sequential()
model.add(LSTM(50,return_sequences=True,input_shape=(time_step,1)))
model.add(LSTM(50,return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss='mean_squared_error',optimizer='adam')

  • tf.keras.backend.clear_session(): This clears any old models or layers that might be in memory.

  • model=Sequential(): This starts building a new model.

  • model.add(LSTM(50,return_sequences=True,input_shape=(time_step,1))): This adds a layer to the model that can remember patterns in a sequence of data. It’s set up to look at time_step number of data points at a time.

  • model.add(LSTM(50,return_sequences=True)): This adds another layer that can remember patterns. This layer takes the output of the previous layer as its input.

  • model.add(LSTM(50)): This adds a third layer that can remember patterns. This layer only returns the final output, not the whole sequence.

  • model.add(Dense(1)): This adds a layer that connects everything together. It takes the output of the previous layer and transforms it into the final prediction.

  • model.compile(loss='mean_squared_error',optimizer='adam'): This sets up the model for training. It will use the ‘mean_squared_error’ method to measure how well the model is doing, and the ‘adam’ method to improve the model.

In [31]:
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 lstm (LSTM)                 (None, 100, 50)           10400     
                                                                 
 lstm_1 (LSTM)               (None, 100, 50)           20200     
                                                                 
 lstm_2 (LSTM)               (None, 50)                20200     
                                                                 
 dense (Dense)               (None, 1)                 51        
                                                                 
=================================================================
Total params: 50851 (198.64 KB)
Trainable params: 50851 (198.64 KB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
In [32]:
model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=100,batch_size=64,verbose=1)

Epoch 1/100
34/34 [==============================] - 20s 359ms/step - loss: 0.0407 - val_loss: 0.0038
Epoch 2/100
34/34 [==============================] - 10s 304ms/step - loss: 0.0024 - val_loss: 2.7771e-04
Epoch 3/100
34/34 [==============================] - 11s 310ms/step - loss: 0.0015 - val_loss: 9.8978e-05
Epoch 4/100
34/34 [==============================] - 11s 314ms/step - loss: 0.0014 - val_loss: 1.1492e-04
Epoch 5/100
34/34 [==============================] - 10s 303ms/step - loss: 0.0014 - val_loss: 9.0473e-05
Epoch 6/100
34/34 [==============================] - 10s 309ms/step - loss: 0.0013 - val_loss: 9.0994e-05
Epoch 7/100
34/34 [==============================] - 11s 312ms/step - loss: 0.0012 - val_loss: 1.0977e-04
Epoch 8/100
34/34 [==============================] - 11s 317ms/step - loss: 0.0012 - val_loss: 8.6356e-05
Epoch 9/100
34/34 [==============================] - 11s 318ms/step - loss: 0.0011 - val_loss: 8.9304e-05
Epoch 10/100
34/34 [==============================] - 10s 307ms/step - loss: 0.0010 - val_loss: 1.3621e-04
Epoch 11/100
34/34 [==============================] - 9s 275ms/step - loss: 9.4824e-04 - val_loss: 1.0106e-04
Epoch 12/100
34/34 [==============================] - 10s 285ms/step - loss: 9.0191e-04 - val_loss: 7.1865e-05
Epoch 13/100
34/34 [==============================] - 11s 318ms/step - loss: 9.6204e-04 - val_loss: 1.3864e-04
Epoch 14/100
34/34 [==============================] - 10s 304ms/step - loss: 8.1078e-04 - val_loss: 8.1983e-05
Epoch 15/100
34/34 [==============================] - 11s 313ms/step - loss: 8.5681e-04 - val_loss: 6.9969e-05
Epoch 16/100
34/34 [==============================] - 10s 308ms/step - loss: 7.7695e-04 - val_loss: 1.0921e-04
Epoch 17/100
34/34 [==============================] - 11s 315ms/step - loss: 7.0167e-04 - val_loss: 6.2738e-05
Epoch 18/100
34/34 [==============================] - 11s 323ms/step - loss: 7.0647e-04 - val_loss: 7.0629e-05
Epoch 19/100
34/34 [==============================] - 11s 319ms/step - loss: 6.9544e-04 - val_loss: 5.6737e-05
Epoch 20/100
34/34 [==============================] - 11s 322ms/step - loss: 6.4603e-04 - val_loss: 7.1928e-05
Epoch 21/100
34/34 [==============================] - 11s 324ms/step - loss: 6.5815e-04 - val_loss: 5.8108e-05
Epoch 22/100
34/34 [==============================] - 11s 319ms/step - loss: 6.1573e-04 - val_loss: 6.9428e-05
Epoch 23/100
34/34 [==============================] - 11s 327ms/step - loss: 6.0622e-04 - val_loss: 5.0099e-05
Epoch 24/100
34/34 [==============================] - 11s 324ms/step - loss: 5.9951e-04 - val_loss: 1.2178e-04
Epoch 25/100
34/34 [==============================] - 11s 315ms/step - loss: 6.8743e-04 - val_loss: 8.7431e-05
Epoch 26/100
34/34 [==============================] - 11s 317ms/step - loss: 5.8004e-04 - val_loss: 6.0611e-05
Epoch 27/100
34/34 [==============================] - 11s 317ms/step - loss: 5.7465e-04 - val_loss: 4.5974e-05
Epoch 28/100
34/34 [==============================] - 11s 312ms/step - loss: 5.3599e-04 - val_loss: 4.9688e-05
Epoch 29/100
34/34 [==============================] - 11s 312ms/step - loss: 5.3899e-04 - val_loss: 4.6389e-05
Epoch 30/100
34/34 [==============================] - 11s 317ms/step - loss: 5.7204e-04 - val_loss: 9.4227e-05
Epoch 31/100
34/34 [==============================] - 10s 292ms/step - loss: 5.1887e-04 - val_loss: 8.6668e-05
Epoch 32/100
34/34 [==============================] - 10s 291ms/step - loss: 5.3073e-04 - val_loss: 4.3157e-05
Epoch 33/100
34/34 [==============================] - 10s 306ms/step - loss: 5.0894e-04 - val_loss: 4.2498e-05
Epoch 34/100
34/34 [==============================] - 10s 295ms/step - loss: 5.0502e-04 - val_loss: 5.1741e-05
Epoch 35/100
34/34 [==============================] - 11s 319ms/step - loss: 4.6187e-04 - val_loss: 5.3396e-05
Epoch 36/100
34/34 [==============================] - 11s 329ms/step - loss: 5.2586e-04 - val_loss: 5.5566e-05
Epoch 37/100
34/34 [==============================] - 11s 318ms/step - loss: 5.2210e-04 - val_loss: 5.3722e-05
Epoch 38/100
34/34 [==============================] - 11s 322ms/step - loss: 4.4619e-04 - val_loss: 5.6954e-05
Epoch 39/100
34/34 [==============================] - 11s 323ms/step - loss: 4.2356e-04 - val_loss: 4.4217e-05
Epoch 40/100
34/34 [==============================] - 10s 302ms/step - loss: 4.5301e-04 - val_loss: 4.7083e-05
Epoch 41/100
34/34 [==============================] - 10s 292ms/step - loss: 4.1361e-04 - val_loss: 3.6614e-05
Epoch 42/100
34/34 [==============================] - 11s 315ms/step - loss: 4.9114e-04 - val_loss: 5.8949e-05
Epoch 43/100
34/34 [==============================] - 10s 310ms/step - loss: 4.9952e-04 - val_loss: 4.8554e-05
Epoch 44/100
34/34 [==============================] - 11s 321ms/step - loss: 4.2608e-04 - val_loss: 5.4590e-05
Epoch 45/100
34/34 [==============================] - 11s 327ms/step - loss: 3.8870e-04 - val_loss: 4.6298e-05
Epoch 46/100
34/34 [==============================] - 11s 322ms/step - loss: 3.7003e-04 - val_loss: 5.2742e-05
Epoch 47/100
34/34 [==============================] - 11s 322ms/step - loss: 3.8733e-04 - val_loss: 4.3734e-05
Epoch 48/100
34/34 [==============================] - 11s 326ms/step - loss: 4.9354e-04 - val_loss: 5.3816e-05
Epoch 49/100
34/34 [==============================] - 11s 313ms/step - loss: 3.6431e-04 - val_loss: 5.9958e-05
Epoch 50/100
34/34 [==============================] - 10s 307ms/step - loss: 3.8859e-04 - val_loss: 3.5130e-05
Epoch 51/100
34/34 [==============================] - 10s 304ms/step - loss: 3.7004e-04 - val_loss: 3.7626e-05
Epoch 52/100
34/34 [==============================] - 10s 294ms/step - loss: 3.6107e-04 - val_loss: 3.6198e-05
Epoch 53/100
34/34 [==============================] - 11s 316ms/step - loss: 3.3055e-04 - val_loss: 3.1521e-05
Epoch 54/100
34/34 [==============================] - 11s 328ms/step - loss: 3.2187e-04 - val_loss: 3.9860e-05
Epoch 55/100
34/34 [==============================] - 11s 327ms/step - loss: 4.1272e-04 - val_loss: 3.0501e-05
Epoch 56/100
34/34 [==============================] - 11s 326ms/step - loss: 3.9849e-04 - val_loss: 6.9342e-05
Epoch 57/100
34/34 [==============================] - 11s 323ms/step - loss: 3.1052e-04 - val_loss: 3.0283e-05
Epoch 58/100
34/34 [==============================] - 11s 330ms/step - loss: 3.3242e-04 - val_loss: 2.9108e-05
Epoch 59/100
34/34 [==============================] - 11s 332ms/step - loss: 3.0027e-04 - val_loss: 3.2512e-05
Epoch 60/100
34/34 [==============================] - 11s 325ms/step - loss: 3.2341e-04 - val_loss: 3.0677e-05
Epoch 61/100
34/34 [==============================] - 11s 331ms/step - loss: 2.8944e-04 - val_loss: 3.5484e-05
Epoch 62/100
34/34 [==============================] - 11s 331ms/step - loss: 3.1990e-04 - val_loss: 2.8282e-05
Epoch 63/100
34/34 [==============================] - 11s 329ms/step - loss: 3.3296e-04 - val_loss: 3.4113e-05
Epoch 64/100
34/34 [==============================] - 11s 327ms/step - loss: 4.6548e-04 - val_loss: 6.1087e-05
Epoch 65/100
34/34 [==============================] - 11s 321ms/step - loss: 3.2797e-04 - val_loss: 4.7215e-05
Epoch 66/100
34/34 [==============================] - 11s 317ms/step - loss: 2.9631e-04 - val_loss: 2.7795e-05
Epoch 67/100
34/34 [==============================] - 11s 329ms/step - loss: 2.8704e-04 - val_loss: 6.0813e-05
Epoch 68/100
34/34 [==============================] - 11s 320ms/step - loss: 3.2117e-04 - val_loss: 3.2808e-05
Epoch 69/100
34/34 [==============================] - 11s 322ms/step - loss: 2.8712e-04 - val_loss: 2.6467e-05
Epoch 70/100
34/34 [==============================] - 11s 331ms/step - loss: 3.1922e-04 - val_loss: 3.0806e-05
Epoch 71/100
34/34 [==============================] - 11s 329ms/step - loss: 3.0502e-04 - val_loss: 2.7545e-05
Epoch 72/100
34/34 [==============================] - 11s 327ms/step - loss: 2.8816e-04 - val_loss: 2.5383e-05
Epoch 73/100
34/34 [==============================] - 11s 319ms/step - loss: 3.2153e-04 - val_loss: 5.6729e-05
Epoch 74/100
34/34 [==============================] - 11s 329ms/step - loss: 2.7231e-04 - val_loss: 3.7355e-05
Epoch 75/100
34/34 [==============================] - 11s 321ms/step - loss: 2.5087e-04 - val_loss: 2.8689e-05
Epoch 76/100
34/34 [==============================] - 11s 325ms/step - loss: 2.6238e-04 - val_loss: 3.1594e-05
Epoch 77/100
34/34 [==============================] - 11s 323ms/step - loss: 2.6235e-04 - val_loss: 2.6123e-05
Epoch 78/100
34/34 [==============================] - 11s 330ms/step - loss: 2.5165e-04 - val_loss: 2.4417e-05
Epoch 79/100
34/34 [==============================] - 11s 325ms/step - loss: 2.6554e-04 - val_loss: 2.3992e-05
Epoch 80/100
34/34 [==============================] - 11s 327ms/step - loss: 2.6998e-04 - val_loss: 8.5000e-05
Epoch 81/100
34/34 [==============================] - 11s 327ms/step - loss: 2.6286e-04 - val_loss: 2.4477e-05
Epoch 82/100
34/34 [==============================] - 8s 247ms/step - loss: 3.7202e-04 - val_loss: 4.9306e-05
Epoch 83/100
34/34 [==============================] - 11s 321ms/step - loss: 2.9772e-04 - val_loss: 2.4775e-05
Epoch 84/100
34/34 [==============================] - 11s 328ms/step - loss: 2.5416e-04 - val_loss: 2.6416e-05
Epoch 85/100
34/34 [==============================] - 111s 3s/step - loss: 2.7058e-04 - val_loss: 3.2514e-05
Epoch 86/100
34/34 [==============================] - 10s 305ms/step - loss: 2.5983e-04 - val_loss: 2.4099e-05
Epoch 87/100
34/34 [==============================] - 11s 322ms/step - loss: 2.9712e-04 - val_loss: 1.0564e-04
Epoch 88/100
34/34 [==============================] - 11s 324ms/step - loss: 3.2716e-04 - val_loss: 2.3412e-05
Epoch 89/100
34/34 [==============================] - 11s 321ms/step - loss: 3.6603e-04 - val_loss: 2.5451e-05
Epoch 90/100
34/34 [==============================] - 11s 328ms/step - loss: 2.7522e-04 - val_loss: 3.7760e-05
Epoch 91/100
34/34 [==============================] - 11s 323ms/step - loss: 2.3231e-04 - val_loss: 2.3983e-05
Epoch 92/100
34/34 [==============================] - 11s 325ms/step - loss: 2.4959e-04 - val_loss: 3.1213e-05
Epoch 93/100
34/34 [==============================] - 11s 331ms/step - loss: 2.7979e-04 - val_loss: 3.6533e-05
Epoch 94/100
34/34 [==============================] - 11s 323ms/step - loss: 2.5164e-04 - val_loss: 3.1772e-05
Epoch 95/100
34/34 [==============================] - 11s 327ms/step - loss: 2.5971e-04 - val_loss: 2.5224e-05
Epoch 96/100
34/34 [==============================] - 11s 327ms/step - loss: 2.4171e-04 - val_loss: 3.3931e-05
Epoch 97/100
34/34 [==============================] - 11s 329ms/step - loss: 2.5325e-04 - val_loss: 2.6800e-05
Epoch 98/100
34/34 [==============================] - 11s 333ms/step - loss: 2.3615e-04 - val_loss: 2.2703e-05
Epoch 99/100
34/34 [==============================] - 11s 329ms/step - loss: 2.4124e-04 - val_loss: 2.3569e-05
Epoch 100/100
34/34 [==============================] - 11s 335ms/step - loss: 2.3454e-04 - val_loss: 2.6811e-05
Out[32]:
<keras.src.callbacks.History at 0x24912aabd00>
In [33]:
### Lets Do the prediction and check performance metrics
train_predict=model.predict(X_train)
test_predict=model.predict(X_test)

67/67 [==============================] - 5s 54ms/step
27/27 [==============================] - 1s 49ms/step
In [34]:
train_predict.shape, test_predict.shape
Out[34]:
((2143, 1), (861, 1))

5.2 Retransformation¶

In [35]:
# Transform back to original form

train_predict = Scaler.inverse_transform(train_predict)
test_predict = Scaler.inverse_transform(test_predict)
In [36]:
original_ytrain = Scaler.inverse_transform(y_train.reshape(-1,1)) 
original_ytest = Scaler.inverse_transform(y_test.reshape(-1,1))
In [37]:
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score 
from sklearn.metrics import mean_poisson_deviance, mean_gamma_deviance, accuracy_score
In [38]:
math.sqrt(mean_squared_error(y_train,train_predict))
Out[38]:
65.88828471327315
In [39]:
math.sqrt(mean_squared_error(y_test,test_predict))
Out[39]:
9.661302158777342
In [40]:
# Evaluation metrices RMSE and MAE
print("Train data RMSE: ", math.sqrt(mean_squared_error(original_ytrain,train_predict)))
print("Train data MSE: ", mean_squared_error(original_ytrain,train_predict))
print("Traint data MAE: ", mean_absolute_error(original_ytrain,train_predict))
print("-------------------------------------------------------------------------------------")
print("Test data RMSE: ", math.sqrt(mean_squared_error(original_ytest,test_predict)))
print("Test data MSE: ", mean_squared_error(original_ytest,test_predict))
print("Test data MAE: ", mean_absolute_error(original_ytest,test_predict))

Train data RMSE:  1.7947650549226102
Train data MSE:  3.22118160237136
Traint data MAE:  1.2684575013328192
-------------------------------------------------------------------------------------
Test data RMSE:  0.6198080165432677
Test data MSE:  0.38416197737129953
Test data MAE:  0.4741334131524665
In [41]:
# shift train predictions for plotting

look_back=time_step
trainPredictPlot = np.empty_like(df1)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict
print("Train predicted data: ", trainPredictPlot.shape)

# shift test predictions for plotting
testPredictPlot = np.empty_like(df1)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict
print("Test predicted data: ", testPredictPlot.shape)

names = cycle(['Original Open price','Train predicted Open price','Test predicted Open price'])


plotdf = pd.DataFrame({'Date': df['Date'],
                       'original_Open': df['Open'],
                      'train_predicted_Open': trainPredictPlot.reshape(1,-1)[0].tolist(),
                      'test_predicted_Open': testPredictPlot.reshape(1,-1)[0].tolist()})

fig = px.line(plotdf,x=plotdf['Date'], y=[plotdf['original_Open'],plotdf['train_predicted_Open'],
                                          plotdf['test_predicted_Open']],
              labels={'value':'Stock price','Date': 'Date'})
fig.update_layout(title_text='Comparision between original Open price vs predicted Open price',
                  plot_bgcolor='white', font_size=15, font_color='black', legend_title_text='Open Price')
fig.for_each_trace(lambda t:  t.update(name = next(names)))

fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
fig.show()

Train predicted data:  (3206, 1)
Test predicted data:  (3206, 1)
In [42]:
len(test_data)
Out[42]:
962
In [43]:
x_input=test_data[862:].reshape(1,-1)
x_input.shape
Out[43]:
(1, 100)
In [44]:
temp_input=list(x_input)
temp_input=temp_input[0].tolist()
In [45]:
temp_input
Out[45]:
[0.037593133856368005,
 0.03717543236907503,
 0.03634002939448906,
 0.04260555170388372,
 0.03926393980553991,
 0.038846238318246934,
 0.04051704426741884,
 0.04552946211493458,
 0.04344095467846969,
 0.0430232531911767,
 0.04260555170388372,
 0.040934745754711815,
 0.03926393980553991,
 0.04177014872929777,
 0.04009934278012586,
 0.04009934278012586,
 0.040934745754711815,
 0.04009934278012586,
 0.03968164129283289,
 0.04009934278012586,
 0.040934745754711815,
 0.04009934278012586,
 0.03968164129283289,
 0.03968164129283289,
 0.04009934278012586,
 0.03926393980553991,
 0.03926393980553991,
 0.038846238318246934,
 0.03801083534366098,
 0.03926393980553991,
 0.040934745754711815,
 0.04177014872929777,
 0.04177014872929777,
 0.040934745754711815,
 0.04385865616576266,
 0.04385865616576266,
 0.04469405914034863,
 0.05304808888620818,
 0.04887107401327841,
 0.04720026806410649,
 0.04720026806410649,
 0.046364865089520535,
 0.04344095467846969,
 0.04344095467846969,
 0.04678256657681351,
 0.047617969551399464,
 0.04720026806410649,
 0.045947163602227545,
 0.04511176062764159,
 0.04511176062764159,
 0.04344095467846969,
 0.042187850216590744,
 0.042187850216590744,
 0.04135244724200479,
 0.04051704426741884,
 0.04260555170388372,
 0.0430232531911767,
 0.04469405914034863,
 0.05179498442432924,
 0.05263038739891519,
 0.05179498442432924,
 0.053883491860794136,
 0.053883491860794136,
 0.0618198201193607,
 0.06140211863206774,
 0.06098441714477475,
 0.06641453647958347,
 0.06557913350499751,
 0.06641453647958347,
 0.07268005878897812,
 0.06516143201770452,
 0.07017384986522025,
 0.0693384468906343,
 0.08186949150942364,
 0.06892074540334132,
 0.06892074540334132,
 0.06850304391604835,
 0.07226235730168515,
 0.07268005878897812,
 0.0789455810983728,
 0.07727477514920088,
 0.07560396920002897,
 0.07560396920002897,
 0.07727477514920088,
 0.07393316325085707,
 0.0710092528398062,
 0.06683223796687643,
 0.0693384468906343,
 0.0710092528398062,
 0.07685707366190792,
 0.07602167068732196,
 0.0785278796110798,
 0.07643937217461493,
 0.07727477514920088,
 0.07435086473815003,
 0.07769247663649387,
 0.07602167068732196,
 0.0693384468906343,
 0.06808534242875537,
 0.06641453647958347]
In [46]:
# demonstrating prediction for next 10 days
from numpy import array

lst_output=[]
n_step=100
i=0
while(i<30):
    
    if(len(temp_input)>100):
        
        x_input=np.array(temp_input[1:])
        print("{} day input {}".format(i,x_input))
        x_input=x_input.reshape(1,-1)
        x_input=x_input.reshape((1,n_step,1))
        
        yhat=model.predict(x_input,verbose=0)
        print("{} day input {}".format(i,yhat))
        temp_input.extend(yhat[0].tolist())
        temp_input=temp_input[1:]
        
        lst_output.extend(yhat.tolist())
        i=i+1
    else:
        x_input=x_input.reshape((1,n_step,1))
        yhat=model.predict(x_input,verbose=0)
        print(yhat[0])
        
        temp_input.extend(yhat[0].tolist())
        print(len(temp_input))
        lst_output.extend(yhat.tolist())
        i=i+1
        
        
print(lst_output)
    

[0.07203833]
101
1 day input [0.03717543 0.03634003 0.04260555 0.03926394 0.03884624 0.04051704
 0.04552946 0.04344095 0.04302325 0.04260555 0.04093475 0.03926394
 0.04177015 0.04009934 0.04009934 0.04093475 0.04009934 0.03968164
 0.04009934 0.04093475 0.04009934 0.03968164 0.03968164 0.04009934
 0.03926394 0.03926394 0.03884624 0.03801084 0.03926394 0.04093475
 0.04177015 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406
 0.05304809 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095
 0.04344095 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176
 0.04511176 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704
 0.04260555 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498
 0.05388349 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454
 0.06557913 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845
 0.08186949 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006
 0.07894558 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316
 0.07100925 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167
 0.07852788 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167
 0.06933845 0.06808534 0.06641454 0.07203833]
1 day input [[0.07127579]]
2 day input [0.03634003 0.04260555 0.03926394 0.03884624 0.04051704 0.04552946
 0.04344095 0.04302325 0.04260555 0.04093475 0.03926394 0.04177015
 0.04009934 0.04009934 0.04093475 0.04009934 0.03968164 0.04009934
 0.04093475 0.04009934 0.03968164 0.03968164 0.04009934 0.03926394
 0.03926394 0.03884624 0.03801084 0.03926394 0.04093475 0.04177015
 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809
 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095 0.04344095
 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176
 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555
 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349
 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913
 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949
 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006 0.07894558
 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925
 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788
 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845
 0.06808534 0.06641454 0.07203833 0.07127579]
2 day input [[0.07144839]]
3 day input [0.04260555 0.03926394 0.03884624 0.04051704 0.04552946 0.04344095
 0.04302325 0.04260555 0.04093475 0.03926394 0.04177015 0.04009934
 0.04009934 0.04093475 0.04009934 0.03968164 0.04009934 0.04093475
 0.04009934 0.03968164 0.03968164 0.04009934 0.03926394 0.03926394
 0.03884624 0.03801084 0.03926394 0.04093475 0.04177015 0.04177015
 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107
 0.04720027 0.04720027 0.04636487 0.04344095 0.04344095 0.04678257
 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095
 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325
 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349
 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454
 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949 0.06892075
 0.06892075 0.06850304 0.07226236 0.07268006 0.07894558 0.07727478
 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925 0.06683224
 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788 0.07643937
 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534
 0.06641454 0.07203833 0.07127579 0.07144839]
3 day input [[0.0720036]]
4 day input [0.03926394 0.03884624 0.04051704 0.04552946 0.04344095 0.04302325
 0.04260555 0.04093475 0.03926394 0.04177015 0.04009934 0.04009934
 0.04093475 0.04009934 0.03968164 0.04009934 0.04093475 0.04009934
 0.03968164 0.03968164 0.04009934 0.03926394 0.03926394 0.03884624
 0.03801084 0.03926394 0.04093475 0.04177015 0.04177015 0.04093475
 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027
 0.04720027 0.04636487 0.04344095 0.04344095 0.04678257 0.04761797
 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785
 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406
 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982
 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006
 0.06516143 0.07017385 0.06933845 0.08186949 0.06892075 0.06892075
 0.06850304 0.07226236 0.07268006 0.07894558 0.07727478 0.07560397
 0.07560397 0.07727478 0.07393316 0.07100925 0.06683224 0.06933845
 0.07100925 0.07685707 0.07602167 0.07852788 0.07643937 0.07727478
 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454
 0.07203833 0.07127579 0.07144839 0.0720036 ]
4 day input [[0.0726905]]
5 day input [0.03884624 0.04051704 0.04552946 0.04344095 0.04302325 0.04260555
 0.04093475 0.03926394 0.04177015 0.04009934 0.04009934 0.04093475
 0.04009934 0.03968164 0.04009934 0.04093475 0.04009934 0.03968164
 0.03968164 0.04009934 0.03926394 0.03926394 0.03884624 0.03801084
 0.03926394 0.04093475 0.04177015 0.04177015 0.04093475 0.04385866
 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027 0.04720027
 0.04636487 0.04344095 0.04344095 0.04678257 0.04761797 0.04720027
 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785 0.04218785
 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406 0.05179498
 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982 0.06140212
 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006 0.06516143
 0.07017385 0.06933845 0.08186949 0.06892075 0.06892075 0.06850304
 0.07226236 0.07268006 0.07894558 0.07727478 0.07560397 0.07560397
 0.07727478 0.07393316 0.07100925 0.06683224 0.06933845 0.07100925
 0.07685707 0.07602167 0.07852788 0.07643937 0.07727478 0.07435086
 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454 0.07203833
 0.07127579 0.07144839 0.0720036  0.0726905 ]
5 day input [[0.07341842]]
6 day input [0.04051704 0.04552946 0.04344095 0.04302325 0.04260555 0.04093475
 0.03926394 0.04177015 0.04009934 0.04009934 0.04093475 0.04009934
 0.03968164 0.04009934 0.04093475 0.04009934 0.03968164 0.03968164
 0.04009934 0.03926394 0.03926394 0.03884624 0.03801084 0.03926394
 0.04093475 0.04177015 0.04177015 0.04093475 0.04385866 0.04385866
 0.04469406 0.05304809 0.04887107 0.04720027 0.04720027 0.04636487
 0.04344095 0.04344095 0.04678257 0.04761797 0.04720027 0.04594716
 0.04511176 0.04511176 0.04344095 0.04218785 0.04218785 0.04135245
 0.04051704 0.04260555 0.04302325 0.04469406 0.05179498 0.05263039
 0.05179498 0.05388349 0.05388349 0.06181982 0.06140212 0.06098442
 0.06641454 0.06557913 0.06641454 0.07268006 0.06516143 0.07017385
 0.06933845 0.08186949 0.06892075 0.06892075 0.06850304 0.07226236
 0.07268006 0.07894558 0.07727478 0.07560397 0.07560397 0.07727478
 0.07393316 0.07100925 0.06683224 0.06933845 0.07100925 0.07685707
 0.07602167 0.07852788 0.07643937 0.07727478 0.07435086 0.07769248
 0.07602167 0.06933845 0.06808534 0.06641454 0.07203833 0.07127579
 0.07144839 0.0720036  0.0726905  0.07341842]
6 day input [[0.07415743]]
7 day input [0.04552946 0.04344095 0.04302325 0.04260555 0.04093475 0.03926394
 0.04177015 0.04009934 0.04009934 0.04093475 0.04009934 0.03968164
 0.04009934 0.04093475 0.04009934 0.03968164 0.03968164 0.04009934
 0.03926394 0.03926394 0.03884624 0.03801084 0.03926394 0.04093475
 0.04177015 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406
 0.05304809 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095
 0.04344095 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176
 0.04511176 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704
 0.04260555 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498
 0.05388349 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454
 0.06557913 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845
 0.08186949 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006
 0.07894558 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316
 0.07100925 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167
 0.07852788 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167
 0.06933845 0.06808534 0.06641454 0.07203833 0.07127579 0.07144839
 0.0720036  0.0726905  0.07341842 0.07415743]
7 day input [[0.0748966]]
8 day input [0.04344095 0.04302325 0.04260555 0.04093475 0.03926394 0.04177015
 0.04009934 0.04009934 0.04093475 0.04009934 0.03968164 0.04009934
 0.04093475 0.04009934 0.03968164 0.03968164 0.04009934 0.03926394
 0.03926394 0.03884624 0.03801084 0.03926394 0.04093475 0.04177015
 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809
 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095 0.04344095
 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176
 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555
 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349
 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913
 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949
 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006 0.07894558
 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925
 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788
 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845
 0.06808534 0.06641454 0.07203833 0.07127579 0.07144839 0.0720036
 0.0726905  0.07341842 0.07415743 0.0748966 ]
8 day input [[0.07563051]]
9 day input [0.04302325 0.04260555 0.04093475 0.03926394 0.04177015 0.04009934
 0.04009934 0.04093475 0.04009934 0.03968164 0.04009934 0.04093475
 0.04009934 0.03968164 0.03968164 0.04009934 0.03926394 0.03926394
 0.03884624 0.03801084 0.03926394 0.04093475 0.04177015 0.04177015
 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107
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 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095
 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325
 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349
 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454
 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949 0.06892075
 0.06892075 0.06850304 0.07226236 0.07268006 0.07894558 0.07727478
 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925 0.06683224
 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788 0.07643937
 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534
 0.06641454 0.07203833 0.07127579 0.07144839 0.0720036  0.0726905
 0.07341842 0.07415743 0.0748966  0.07563051]
9 day input [[0.07635623]]
10 day input [0.04260555 0.04093475 0.03926394 0.04177015 0.04009934 0.04009934
 0.04093475 0.04009934 0.03968164 0.04009934 0.04093475 0.04009934
 0.03968164 0.03968164 0.04009934 0.03926394 0.03926394 0.03884624
 0.03801084 0.03926394 0.04093475 0.04177015 0.04177015 0.04093475
 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027
 0.04720027 0.04636487 0.04344095 0.04344095 0.04678257 0.04761797
 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785
 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406
 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982
 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006
 0.06516143 0.07017385 0.06933845 0.08186949 0.06892075 0.06892075
 0.06850304 0.07226236 0.07268006 0.07894558 0.07727478 0.07560397
 0.07560397 0.07727478 0.07393316 0.07100925 0.06683224 0.06933845
 0.07100925 0.07685707 0.07602167 0.07852788 0.07643937 0.07727478
 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454
 0.07203833 0.07127579 0.07144839 0.0720036  0.0726905  0.07341842
 0.07415743 0.0748966  0.07563051 0.07635623]
10 day input [[0.07707241]]
11 day input [0.04093475 0.03926394 0.04177015 0.04009934 0.04009934 0.04093475
 0.04009934 0.03968164 0.04009934 0.04093475 0.04009934 0.03968164
 0.03968164 0.04009934 0.03926394 0.03926394 0.03884624 0.03801084
 0.03926394 0.04093475 0.04177015 0.04177015 0.04093475 0.04385866
 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027 0.04720027
 0.04636487 0.04344095 0.04344095 0.04678257 0.04761797 0.04720027
 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785 0.04218785
 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406 0.05179498
 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982 0.06140212
 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006 0.06516143
 0.07017385 0.06933845 0.08186949 0.06892075 0.06892075 0.06850304
 0.07226236 0.07268006 0.07894558 0.07727478 0.07560397 0.07560397
 0.07727478 0.07393316 0.07100925 0.06683224 0.06933845 0.07100925
 0.07685707 0.07602167 0.07852788 0.07643937 0.07727478 0.07435086
 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454 0.07203833
 0.07127579 0.07144839 0.0720036  0.0726905  0.07341842 0.07415743
 0.0748966  0.07563051 0.07635623 0.07707241]
11 day input [[0.07777882]]
12 day input [0.03926394 0.04177015 0.04009934 0.04009934 0.04093475 0.04009934
 0.03968164 0.04009934 0.04093475 0.04009934 0.03968164 0.03968164
 0.04009934 0.03926394 0.03926394 0.03884624 0.03801084 0.03926394
 0.04093475 0.04177015 0.04177015 0.04093475 0.04385866 0.04385866
 0.04469406 0.05304809 0.04887107 0.04720027 0.04720027 0.04636487
 0.04344095 0.04344095 0.04678257 0.04761797 0.04720027 0.04594716
 0.04511176 0.04511176 0.04344095 0.04218785 0.04218785 0.04135245
 0.04051704 0.04260555 0.04302325 0.04469406 0.05179498 0.05263039
 0.05179498 0.05388349 0.05388349 0.06181982 0.06140212 0.06098442
 0.06641454 0.06557913 0.06641454 0.07268006 0.06516143 0.07017385
 0.06933845 0.08186949 0.06892075 0.06892075 0.06850304 0.07226236
 0.07268006 0.07894558 0.07727478 0.07560397 0.07560397 0.07727478
 0.07393316 0.07100925 0.06683224 0.06933845 0.07100925 0.07685707
 0.07602167 0.07852788 0.07643937 0.07727478 0.07435086 0.07769248
 0.07602167 0.06933845 0.06808534 0.06641454 0.07203833 0.07127579
 0.07144839 0.0720036  0.0726905  0.07341842 0.07415743 0.0748966
 0.07563051 0.07635623 0.07707241 0.07777882]
12 day input [[0.07847603]]
13 day input [0.04177015 0.04009934 0.04009934 0.04093475 0.04009934 0.03968164
 0.04009934 0.04093475 0.04009934 0.03968164 0.03968164 0.04009934
 0.03926394 0.03926394 0.03884624 0.03801084 0.03926394 0.04093475
 0.04177015 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406
 0.05304809 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095
 0.04344095 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176
 0.04511176 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704
 0.04260555 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498
 0.05388349 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454
 0.06557913 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845
 0.08186949 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006
 0.07894558 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316
 0.07100925 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167
 0.07852788 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167
 0.06933845 0.06808534 0.06641454 0.07203833 0.07127579 0.07144839
 0.0720036  0.0726905  0.07341842 0.07415743 0.0748966  0.07563051
 0.07635623 0.07707241 0.07777882 0.07847603]
13 day input [[0.07916494]]
14 day input [0.04009934 0.04009934 0.04093475 0.04009934 0.03968164 0.04009934
 0.04093475 0.04009934 0.03968164 0.03968164 0.04009934 0.03926394
 0.03926394 0.03884624 0.03801084 0.03926394 0.04093475 0.04177015
 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809
 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095 0.04344095
 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176
 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555
 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349
 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913
 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949
 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006 0.07894558
 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925
 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788
 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845
 0.06808534 0.06641454 0.07203833 0.07127579 0.07144839 0.0720036
 0.0726905  0.07341842 0.07415743 0.0748966  0.07563051 0.07635623
 0.07707241 0.07777882 0.07847603 0.07916494]
14 day input [[0.07984665]]
15 day input [0.04009934 0.04093475 0.04009934 0.03968164 0.04009934 0.04093475
 0.04009934 0.03968164 0.03968164 0.04009934 0.03926394 0.03926394
 0.03884624 0.03801084 0.03926394 0.04093475 0.04177015 0.04177015
 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107
 0.04720027 0.04720027 0.04636487 0.04344095 0.04344095 0.04678257
 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095
 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325
 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349
 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454
 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949 0.06892075
 0.06892075 0.06850304 0.07226236 0.07268006 0.07894558 0.07727478
 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925 0.06683224
 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788 0.07643937
 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534
 0.06641454 0.07203833 0.07127579 0.07144839 0.0720036  0.0726905
 0.07341842 0.07415743 0.0748966  0.07563051 0.07635623 0.07707241
 0.07777882 0.07847603 0.07916494 0.07984665]
15 day input [[0.08052213]]
16 day input [0.04093475 0.04009934 0.03968164 0.04009934 0.04093475 0.04009934
 0.03968164 0.03968164 0.04009934 0.03926394 0.03926394 0.03884624
 0.03801084 0.03926394 0.04093475 0.04177015 0.04177015 0.04093475
 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027
 0.04720027 0.04636487 0.04344095 0.04344095 0.04678257 0.04761797
 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785
 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406
 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982
 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006
 0.06516143 0.07017385 0.06933845 0.08186949 0.06892075 0.06892075
 0.06850304 0.07226236 0.07268006 0.07894558 0.07727478 0.07560397
 0.07560397 0.07727478 0.07393316 0.07100925 0.06683224 0.06933845
 0.07100925 0.07685707 0.07602167 0.07852788 0.07643937 0.07727478
 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454
 0.07203833 0.07127579 0.07144839 0.0720036  0.0726905  0.07341842
 0.07415743 0.0748966  0.07563051 0.07635623 0.07707241 0.07777882
 0.07847603 0.07916494 0.07984665 0.08052213]
16 day input [[0.0811922]]
17 day input [0.04009934 0.03968164 0.04009934 0.04093475 0.04009934 0.03968164
 0.03968164 0.04009934 0.03926394 0.03926394 0.03884624 0.03801084
 0.03926394 0.04093475 0.04177015 0.04177015 0.04093475 0.04385866
 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027 0.04720027
 0.04636487 0.04344095 0.04344095 0.04678257 0.04761797 0.04720027
 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785 0.04218785
 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406 0.05179498
 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982 0.06140212
 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006 0.06516143
 0.07017385 0.06933845 0.08186949 0.06892075 0.06892075 0.06850304
 0.07226236 0.07268006 0.07894558 0.07727478 0.07560397 0.07560397
 0.07727478 0.07393316 0.07100925 0.06683224 0.06933845 0.07100925
 0.07685707 0.07602167 0.07852788 0.07643937 0.07727478 0.07435086
 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454 0.07203833
 0.07127579 0.07144839 0.0720036  0.0726905  0.07341842 0.07415743
 0.0748966  0.07563051 0.07635623 0.07707241 0.07777882 0.07847603
 0.07916494 0.07984665 0.08052213 0.0811922 ]
17 day input [[0.08185746]]
18 day input [0.03968164 0.04009934 0.04093475 0.04009934 0.03968164 0.03968164
 0.04009934 0.03926394 0.03926394 0.03884624 0.03801084 0.03926394
 0.04093475 0.04177015 0.04177015 0.04093475 0.04385866 0.04385866
 0.04469406 0.05304809 0.04887107 0.04720027 0.04720027 0.04636487
 0.04344095 0.04344095 0.04678257 0.04761797 0.04720027 0.04594716
 0.04511176 0.04511176 0.04344095 0.04218785 0.04218785 0.04135245
 0.04051704 0.04260555 0.04302325 0.04469406 0.05179498 0.05263039
 0.05179498 0.05388349 0.05388349 0.06181982 0.06140212 0.06098442
 0.06641454 0.06557913 0.06641454 0.07268006 0.06516143 0.07017385
 0.06933845 0.08186949 0.06892075 0.06892075 0.06850304 0.07226236
 0.07268006 0.07894558 0.07727478 0.07560397 0.07560397 0.07727478
 0.07393316 0.07100925 0.06683224 0.06933845 0.07100925 0.07685707
 0.07602167 0.07852788 0.07643937 0.07727478 0.07435086 0.07769248
 0.07602167 0.06933845 0.06808534 0.06641454 0.07203833 0.07127579
 0.07144839 0.0720036  0.0726905  0.07341842 0.07415743 0.0748966
 0.07563051 0.07635623 0.07707241 0.07777882 0.07847603 0.07916494
 0.07984665 0.08052213 0.0811922  0.08185746]
18 day input [[0.08251831]]
19 day input [0.04009934 0.04093475 0.04009934 0.03968164 0.03968164 0.04009934
 0.03926394 0.03926394 0.03884624 0.03801084 0.03926394 0.04093475
 0.04177015 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406
 0.05304809 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095
 0.04344095 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176
 0.04511176 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704
 0.04260555 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498
 0.05388349 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454
 0.06557913 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845
 0.08186949 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006
 0.07894558 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316
 0.07100925 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167
 0.07852788 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167
 0.06933845 0.06808534 0.06641454 0.07203833 0.07127579 0.07144839
 0.0720036  0.0726905  0.07341842 0.07415743 0.0748966  0.07563051
 0.07635623 0.07707241 0.07777882 0.07847603 0.07916494 0.07984665
 0.08052213 0.0811922  0.08185746 0.08251831]
19 day input [[0.08317492]]
20 day input [0.04093475 0.04009934 0.03968164 0.03968164 0.04009934 0.03926394
 0.03926394 0.03884624 0.03801084 0.03926394 0.04093475 0.04177015
 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809
 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095 0.04344095
 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176
 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555
 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349
 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913
 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949
 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006 0.07894558
 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925
 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788
 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845
 0.06808534 0.06641454 0.07203833 0.07127579 0.07144839 0.0720036
 0.0726905  0.07341842 0.07415743 0.0748966  0.07563051 0.07635623
 0.07707241 0.07777882 0.07847603 0.07916494 0.07984665 0.08052213
 0.0811922  0.08185746 0.08251831 0.08317492]
20 day input [[0.08382733]]
21 day input [0.04009934 0.03968164 0.03968164 0.04009934 0.03926394 0.03926394
 0.03884624 0.03801084 0.03926394 0.04093475 0.04177015 0.04177015
 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107
 0.04720027 0.04720027 0.04636487 0.04344095 0.04344095 0.04678257
 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095
 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325
 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349
 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454
 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949 0.06892075
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 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925 0.06683224
 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788 0.07643937
 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534
 0.06641454 0.07203833 0.07127579 0.07144839 0.0720036  0.0726905
 0.07341842 0.07415743 0.0748966  0.07563051 0.07635623 0.07707241
 0.07777882 0.07847603 0.07916494 0.07984665 0.08052213 0.0811922
 0.08185746 0.08251831 0.08317492 0.08382733]
21 day input [[0.08447549]]
22 day input [0.03968164 0.03968164 0.04009934 0.03926394 0.03926394 0.03884624
 0.03801084 0.03926394 0.04093475 0.04177015 0.04177015 0.04093475
 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027
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 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785
 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406
 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982
 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006
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 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454
 0.07203833 0.07127579 0.07144839 0.0720036  0.0726905  0.07341842
 0.07415743 0.0748966  0.07563051 0.07635623 0.07707241 0.07777882
 0.07847603 0.07916494 0.07984665 0.08052213 0.0811922  0.08185746
 0.08251831 0.08317492 0.08382733 0.08447549]
22 day input [[0.08511929]]
23 day input [0.03968164 0.04009934 0.03926394 0.03926394 0.03884624 0.03801084
 0.03926394 0.04093475 0.04177015 0.04177015 0.04093475 0.04385866
 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027 0.04720027
 0.04636487 0.04344095 0.04344095 0.04678257 0.04761797 0.04720027
 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785 0.04218785
 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406 0.05179498
 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982 0.06140212
 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006 0.06516143
 0.07017385 0.06933845 0.08186949 0.06892075 0.06892075 0.06850304
 0.07226236 0.07268006 0.07894558 0.07727478 0.07560397 0.07560397
 0.07727478 0.07393316 0.07100925 0.06683224 0.06933845 0.07100925
 0.07685707 0.07602167 0.07852788 0.07643937 0.07727478 0.07435086
 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454 0.07203833
 0.07127579 0.07144839 0.0720036  0.0726905  0.07341842 0.07415743
 0.0748966  0.07563051 0.07635623 0.07707241 0.07777882 0.07847603
 0.07916494 0.07984665 0.08052213 0.0811922  0.08185746 0.08251831
 0.08317492 0.08382733 0.08447549 0.08511929]
23 day input [[0.08575858]]
24 day input [0.04009934 0.03926394 0.03926394 0.03884624 0.03801084 0.03926394
 0.04093475 0.04177015 0.04177015 0.04093475 0.04385866 0.04385866
 0.04469406 0.05304809 0.04887107 0.04720027 0.04720027 0.04636487
 0.04344095 0.04344095 0.04678257 0.04761797 0.04720027 0.04594716
 0.04511176 0.04511176 0.04344095 0.04218785 0.04218785 0.04135245
 0.04051704 0.04260555 0.04302325 0.04469406 0.05179498 0.05263039
 0.05179498 0.05388349 0.05388349 0.06181982 0.06140212 0.06098442
 0.06641454 0.06557913 0.06641454 0.07268006 0.06516143 0.07017385
 0.06933845 0.08186949 0.06892075 0.06892075 0.06850304 0.07226236
 0.07268006 0.07894558 0.07727478 0.07560397 0.07560397 0.07727478
 0.07393316 0.07100925 0.06683224 0.06933845 0.07100925 0.07685707
 0.07602167 0.07852788 0.07643937 0.07727478 0.07435086 0.07769248
 0.07602167 0.06933845 0.06808534 0.06641454 0.07203833 0.07127579
 0.07144839 0.0720036  0.0726905  0.07341842 0.07415743 0.0748966
 0.07563051 0.07635623 0.07707241 0.07777882 0.07847603 0.07916494
 0.07984665 0.08052213 0.0811922  0.08185746 0.08251831 0.08317492
 0.08382733 0.08447549 0.08511929 0.08575858]
24 day input [[0.08639324]]
25 day input [0.03926394 0.03926394 0.03884624 0.03801084 0.03926394 0.04093475
 0.04177015 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406
 0.05304809 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095
 0.04344095 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176
 0.04511176 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704
 0.04260555 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498
 0.05388349 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454
 0.06557913 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845
 0.08186949 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006
 0.07894558 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316
 0.07100925 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167
 0.07852788 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167
 0.06933845 0.06808534 0.06641454 0.07203833 0.07127579 0.07144839
 0.0720036  0.0726905  0.07341842 0.07415743 0.0748966  0.07563051
 0.07635623 0.07707241 0.07777882 0.07847603 0.07916494 0.07984665
 0.08052213 0.0811922  0.08185746 0.08251831 0.08317492 0.08382733
 0.08447549 0.08511929 0.08575858 0.08639324]
25 day input [[0.08702318]]
26 day input [0.03926394 0.03884624 0.03801084 0.03926394 0.04093475 0.04177015
 0.04177015 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809
 0.04887107 0.04720027 0.04720027 0.04636487 0.04344095 0.04344095
 0.04678257 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176
 0.04344095 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555
 0.04302325 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349
 0.05388349 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913
 0.06641454 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949
 0.06892075 0.06892075 0.06850304 0.07226236 0.07268006 0.07894558
 0.07727478 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925
 0.06683224 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788
 0.07643937 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845
 0.06808534 0.06641454 0.07203833 0.07127579 0.07144839 0.0720036
 0.0726905  0.07341842 0.07415743 0.0748966  0.07563051 0.07635623
 0.07707241 0.07777882 0.07847603 0.07916494 0.07984665 0.08052213
 0.0811922  0.08185746 0.08251831 0.08317492 0.08382733 0.08447549
 0.08511929 0.08575858 0.08639324 0.08702318]
26 day input [[0.08764835]]
27 day input [0.03884624 0.03801084 0.03926394 0.04093475 0.04177015 0.04177015
 0.04093475 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107
 0.04720027 0.04720027 0.04636487 0.04344095 0.04344095 0.04678257
 0.04761797 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095
 0.04218785 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325
 0.04469406 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349
 0.06181982 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454
 0.07268006 0.06516143 0.07017385 0.06933845 0.08186949 0.06892075
 0.06892075 0.06850304 0.07226236 0.07268006 0.07894558 0.07727478
 0.07560397 0.07560397 0.07727478 0.07393316 0.07100925 0.06683224
 0.06933845 0.07100925 0.07685707 0.07602167 0.07852788 0.07643937
 0.07727478 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534
 0.06641454 0.07203833 0.07127579 0.07144839 0.0720036  0.0726905
 0.07341842 0.07415743 0.0748966  0.07563051 0.07635623 0.07707241
 0.07777882 0.07847603 0.07916494 0.07984665 0.08052213 0.0811922
 0.08185746 0.08251831 0.08317492 0.08382733 0.08447549 0.08511929
 0.08575858 0.08639324 0.08702318 0.08764835]
27 day input [[0.0882688]]
28 day input [0.03801084 0.03926394 0.04093475 0.04177015 0.04177015 0.04093475
 0.04385866 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027
 0.04720027 0.04636487 0.04344095 0.04344095 0.04678257 0.04761797
 0.04720027 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785
 0.04218785 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406
 0.05179498 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982
 0.06140212 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006
 0.06516143 0.07017385 0.06933845 0.08186949 0.06892075 0.06892075
 0.06850304 0.07226236 0.07268006 0.07894558 0.07727478 0.07560397
 0.07560397 0.07727478 0.07393316 0.07100925 0.06683224 0.06933845
 0.07100925 0.07685707 0.07602167 0.07852788 0.07643937 0.07727478
 0.07435086 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454
 0.07203833 0.07127579 0.07144839 0.0720036  0.0726905  0.07341842
 0.07415743 0.0748966  0.07563051 0.07635623 0.07707241 0.07777882
 0.07847603 0.07916494 0.07984665 0.08052213 0.0811922  0.08185746
 0.08251831 0.08317492 0.08382733 0.08447549 0.08511929 0.08575858
 0.08639324 0.08702318 0.08764835 0.0882688 ]
28 day input [[0.08888458]]
29 day input [0.03926394 0.04093475 0.04177015 0.04177015 0.04093475 0.04385866
 0.04385866 0.04469406 0.05304809 0.04887107 0.04720027 0.04720027
 0.04636487 0.04344095 0.04344095 0.04678257 0.04761797 0.04720027
 0.04594716 0.04511176 0.04511176 0.04344095 0.04218785 0.04218785
 0.04135245 0.04051704 0.04260555 0.04302325 0.04469406 0.05179498
 0.05263039 0.05179498 0.05388349 0.05388349 0.06181982 0.06140212
 0.06098442 0.06641454 0.06557913 0.06641454 0.07268006 0.06516143
 0.07017385 0.06933845 0.08186949 0.06892075 0.06892075 0.06850304
 0.07226236 0.07268006 0.07894558 0.07727478 0.07560397 0.07560397
 0.07727478 0.07393316 0.07100925 0.06683224 0.06933845 0.07100925
 0.07685707 0.07602167 0.07852788 0.07643937 0.07727478 0.07435086
 0.07769248 0.07602167 0.06933845 0.06808534 0.06641454 0.07203833
 0.07127579 0.07144839 0.0720036  0.0726905  0.07341842 0.07415743
 0.0748966  0.07563051 0.07635623 0.07707241 0.07777882 0.07847603
 0.07916494 0.07984665 0.08052213 0.0811922  0.08185746 0.08251831
 0.08317492 0.08382733 0.08447549 0.08511929 0.08575858 0.08639324
 0.08702318 0.08764835 0.0882688  0.08888458]
29 day input [[0.08949577]]
[[0.07203833013772964], [0.07127578556537628], [0.07144839316606522], [0.07200359553098679], [0.07269050180912018], [0.07341842353343964], [0.07415743172168732], [0.07489660382270813], [0.07563050836324692], [0.07635623216629028], [0.07707241177558899], [0.07777882367372513], [0.07847602665424347], [0.07916494458913803], [0.07984665036201477], [0.08052213490009308], [0.08119219541549683], [0.08185745775699615], [0.0825183093547821], [0.0831749215722084], [0.08382733166217804], [0.08447548747062683], [0.08511929214000702], [0.08575858175754547], [0.08639323711395264], [0.08702318370342255], [0.08764834702014923], [0.08826880156993866], [0.08888457715511322], [0.08949577063322067]]
In [47]:
day_new=np.arange(1,101)
day_pred=np.arange(101,131)
In [48]:
len(df1)
Out[48]:
3206
In [49]:
df3=df1.tolist()
df3.extend(lst_output)
In [50]:
plt.plot(day_new,Scaler.inverse_transform(df1[3106:]))
plt.plot(day_pred,Scaler.inverse_transform(lst_output))
Out[50]:
[<matplotlib.lines.Line2D at 0x2491c26dd60>]
In [52]:
df3=df1.tolist()
df3.extend(lst_output)
plt.plot(df3[3100:])
Out[52]:
[<matplotlib.lines.Line2D at 0x2491c34cc40>]
In [61]:
x_input=test_data[len(test_data)-time_step:].reshape(1,-1)
temp_input=list(x_input)
temp_input=temp_input[0].tolist()

from numpy import array

lst_output=[]
n_steps=time_step
i=0
pred_days = 30
while(i<pred_days):
    
    if(len(temp_input)>time_step):
        
        x_input=np.array(temp_input[1:])
        #print("{} day input {}".format(i,x_input))
        x_input = x_input.reshape(1,-1)
        x_input = x_input.reshape((1, n_steps, 1))
        
        yhat = model.predict(x_input, verbose=0)
        #print("{} day output {}".format(i,yhat))
        temp_input.extend(yhat[0].tolist())
        temp_input=temp_input[1:]
        #print(temp_input)
       
        lst_output.extend(yhat.tolist())
        i=i+1
        
    else:
        
        x_input = x_input.reshape((1, n_steps,1))
        yhat = model.predict(x_input, verbose=0)
        temp_input.extend(yhat[0].tolist())
        
        lst_output.extend(yhat.tolist())
        i=i+1
               
print("Output of predicted next days: ", len(lst_output))

Output of predicted next days:  30
In [62]:
last_days=np.arange(1,time_step+1)
day_pred=np.arange(time_step+1,time_step+pred_days+1)
print(last_days)
print(day_pred)

[  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18
  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36
  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54
  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71  72
  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89  90
  91  92  93  94  95  96  97  98  99 100]
[101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118
 119 120 121 122 123 124 125 126 127 128 129 130]
In [63]:
temp_mat = np.empty((len(last_days)+pred_days+1,1))
temp_mat[:] = np.nan
temp_mat = temp_mat.reshape(1,-1).tolist()[0]

last_original_days_value = temp_mat
next_predicted_days_value = temp_mat

last_original_days_value[0:time_step+1] = Scaler.inverse_transform(df1[len(df1)-time_step:]).reshape(1,-1).tolist()[0]
next_predicted_days_value[time_step+1:] = Scaler.inverse_transform(np.array(lst_output).reshape(-1,1)).reshape(1,-1).tolist()[0]

new_pred_plot = pd.DataFrame({
    'last_original_days_value':last_original_days_value,
    'next_predicted_days_value':next_predicted_days_value
})
names = cycle(['Last 100 days close price','Predicted next 30 days close price'])

fig = px.line(new_pred_plot,x=new_pred_plot.index, y=[new_pred_plot['last_original_days_value'],
                                                      new_pred_plot['next_predicted_days_value']],
              labels={'value': 'Stock price','index': 'Timestamp'})
fig.update_layout(title_text='Compare last 100 days vs next 30 days',
                  plot_bgcolor='white', font_size=15, font_color='black', legend_title_text='Close Price')
fig.for_each_trace(lambda t:  t.update(name = next(names)))

fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
fig.show()

Screenshot%202023-11-20%20121428.png

Insight¶

  • The final output showns the increasing trend.
  • Original share price also has increasing trend.
In [56]:
lstmgrudf=df1.tolist()
lstmgrudf.extend((np.array(lst_output).reshape(-1,1)).tolist())
lstmgrudf=Scaler.inverse_transform(lstmgrudf).reshape(1,-1).tolist()[0]

names = cycle(['Close price'])

fig = px.line(lstmgrudf,labels={'value': 'Stock price','index': 'Timestamp'})
fig.update_layout(title_text='Plotting whole closing stock price with prediction',
                  plot_bgcolor='white', font_size=15, font_color='black', legend_title_text='Stock')
fig.for_each_trace(lambda t:  t.update(name = next(names)))

fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
fig.show()